1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2013 Christoph Hertzberg <chtz@informatik.uni-bremen.de>
5 //
6 // This Source Code Form is subject to the terms of the Mozilla
7 // Public License v. 2.0. If a copy of the MPL was not distributed
8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9
10 #include "main.h"
11 #include <unsupported/Eigen/AutoDiff>
12
13 /*
14 * In this file scalar derivations are tested for correctness.
15 * TODO add more tests!
16 */
17
check_atan2()18 template<typename Scalar> void check_atan2()
19 {
20 typedef Matrix<Scalar, 1, 1> Deriv1;
21 typedef AutoDiffScalar<Deriv1> AD;
22
23 AD x(internal::random<Scalar>(-3.0, 3.0), Deriv1::UnitX());
24
25 using std::exp;
26 Scalar r = exp(internal::random<Scalar>(-10, 10));
27
28 AD s = sin(x), c = cos(x);
29 AD res = atan2(r*s, r*c);
30
31 VERIFY_IS_APPROX(res.value(), x.value());
32 VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
33
34 res = atan2(r*s+0, r*c+0);
35 VERIFY_IS_APPROX(res.value(), x.value());
36 VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
37 }
38
check_hyperbolic_functions()39 template<typename Scalar> void check_hyperbolic_functions()
40 {
41 using std::sinh;
42 using std::cosh;
43 using std::tanh;
44 typedef Matrix<Scalar, 1, 1> Deriv1;
45 typedef AutoDiffScalar<Deriv1> AD;
46 Deriv1 p = Deriv1::Random();
47 AD val(p.x(),Deriv1::UnitX());
48
49 Scalar cosh_px = std::cosh(p.x());
50 AD res1 = tanh(val);
51 VERIFY_IS_APPROX(res1.value(), std::tanh(p.x()));
52 VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(1.0) / (cosh_px * cosh_px));
53
54 AD res2 = sinh(val);
55 VERIFY_IS_APPROX(res2.value(), std::sinh(p.x()));
56 VERIFY_IS_APPROX(res2.derivatives().x(), cosh_px);
57
58 AD res3 = cosh(val);
59 VERIFY_IS_APPROX(res3.value(), cosh_px);
60 VERIFY_IS_APPROX(res3.derivatives().x(), std::sinh(p.x()));
61
62 // Check constant values.
63 const Scalar sample_point = Scalar(1) / Scalar(3);
64 val = AD(sample_point,Deriv1::UnitX());
65 res1 = tanh(val);
66 VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(0.896629559604914));
67
68 res2 = sinh(val);
69 VERIFY_IS_APPROX(res2.derivatives().x(), Scalar(1.056071867829939));
70
71 res3 = cosh(val);
72 VERIFY_IS_APPROX(res3.derivatives().x(), Scalar(0.339540557256150));
73 }
74
75 template <typename Scalar>
check_limits_specialization()76 void check_limits_specialization()
77 {
78 typedef Eigen::Matrix<Scalar, 1, 1> Deriv;
79 typedef Eigen::AutoDiffScalar<Deriv> AD;
80
81 typedef std::numeric_limits<AD> A;
82 typedef std::numeric_limits<Scalar> B;
83
84 // workaround "unsed typedef" warning:
85 VERIFY(!bool(internal::is_same<B, A>::value));
86
87 #if EIGEN_HAS_CXX11
88 VERIFY(bool(std::is_base_of<B, A>::value));
89 #endif
90 }
91
test_autodiff_scalar()92 void test_autodiff_scalar()
93 {
94 for(int i = 0; i < g_repeat; i++) {
95 CALL_SUBTEST_1( check_atan2<float>() );
96 CALL_SUBTEST_2( check_atan2<double>() );
97 CALL_SUBTEST_3( check_hyperbolic_functions<float>() );
98 CALL_SUBTEST_4( check_hyperbolic_functions<double>() );
99 CALL_SUBTEST_5( check_limits_specialization<double>());
100 }
101 }
102